Abstract
Bradycardia and tachycardia reflect abnormalities of the heart that can lead to severe harm to the cardiovascular system. The pulsatile signal is a useful tool to detect these two kinds of arrhythmias. In this study, we present a pulsatile synthesis-by-analysis (PSA) modeling based method to detect bradycardia and tachycardia. A new PSA modeling method was proposed to quantitively describe the changes of pulsatile waves, and we obtained twelve parameters for constructing a feature vector from the PSA model of each wave, by which we trained classifiers of probabilistic neural network (PNN) and random forest (RF). Our experiments were performed on the Fantasia and 2015 PhysioNet/CinC Challenge databases. Some pathological and physiological changes were extracted from the average models of the subjects in different groups. The two-sample ks-test results show that all the parameters between different groups are all markedly different (h = 1, p <; 0.05). The classification results show that the performances of RF classifiers are better than that of PNN. The kappa coefficients (KC) of RF classifiers are all over 97%, and that of the classifying among bradycardia, tachycardia, and healthy subjects is 98.652 ± 0.217%. Compared with the performance of some former methods, the obtained results demonstrate that the presented method promotes the classification performance remarkably and has the potential to diagnose bradycardia and tachycardia in m-health.
Highlights
Bradycardia and tachycardia, two critical symptoms of some cardiovascular diseases (CVDs), have caught the attention of many researchers in recent years
In this study, the change of pulsatile wave with aging and arrhythmias is quantitatively described by a pulsatile synthesis-by-analysis (PSA) modeling method we proposed, by which twelve parameters obtained from each pulsatile wave are employed as a feature vector to classify the pulsatile waves from different groups
We used the PSA modeling method to quantitatively describe the change of pulsatile waves, and twelve parameters about the heartbeat rhythm and homodynamic were extracted from the model of each pulsatile wave
Summary
Bradycardia and tachycardia, two critical symptoms of some cardiovascular diseases (CVDs), have caught the attention of many researchers in recent years. With the continuous degradation of heart function, these two arrhythmias may deteriorate into two life-threatening malignant arrhythmias, the extreme bradycardia (EB) with the heart rate lower than 40 beats per minute (bpm) for 5 consecutive beats, and the extreme tachycardia (ET) with the heart rate higher than. 140 bpm for 17 consecutive beats [1]. They may lead to acute CVDs, such as sudden cardiac death [2]. Since diagnosis in the hospital is limited, it is important to accurately detect bradycardia and tachycardia in daily life before they deteriorate into EB and ET. Electrocardiogram (ECG) plays an important role in the diagnosis of CVDs, some indexes extracted from its morphology (R wave [4], QRS complexes [5], T wave, P wave [6], U wave [7], approximate entropy [8]) or/and heartbeat intervals (HBIs)
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